Model selection for physiology

Physiological response parameters were assessed using mixed-effects linear models across species and treatments. Model selection was carried out using backward elimination of random-effects followed by fixed-effects using the package lmerTest (version 3.1.3)

Model selection per parameter

Protein

While value ~ species + fpco2 + ftemp + (1 | colony) + species:ftemp was the best-fit model structure identified, we wanted to model both pCO2 and temperature responses by species so moving forward we are using the following model structure:

value ~ species * (fpco2 + ftemp) + (1 | colony)


Figure:

Carbohydrate

While value ~ species + ftemp was the best-fit model structure identified, we wanted to model responses with a random effect of colony so moving forward we are using the following model structure:

value ~ species * (fpco2 + ftemp) + (1 | colony)


Figure:

Lipid

While value ~ species + ftemp + reef + species:ftemp + species:reef was the best-fit model structure identified, we wanted to model both pCO2 and temperature responses by species and with random effect of colony so moving forward we are using the following model structure:

value ~ species * (fpco2 + ftemp) + reef + species:reef + (1 | colony)


Figure:

Density

While value ~ species + fpco2 + ftemp + reef + (1 | colony) + species:fpco2 + species:ftemp + fpco2:ftemp + species:reef + fpco2:reef + ftemp:reef + species:fpco2:ftemp + species:fpco2:reef + species:ftemp:reef + fpco2:ftemp:reef + species:fpco2:ftemp:reef was the best-fit model structure identified, we wanted to model both pCO2 and temperature responses by species and with random effect of colony so moving forward we are using the following model structure:

value ~ species * (fpco2 + ftemp) + reef + species:reef + (1 | colony)


Figure:

Chlorophyll

Since the best-fit model fits our design, we will proceed with the following model structure:

value ~ species + fpco2 + ftemp + reef + (1 | colony) + species:fpco2 + species:ftemp + fpco2:ftemp + species:reef


Figure:

Total Host

Since the best-fit model fits our design, we will proceed with the following model structure:

value ~ species + fpco2 + ftemp + reef + (1 | colony) + species:fpco2 + species:ftemp + species:reef + fpco2:reef + species:fpco2:reef


Figure:

Calcification

This is the same model from Bove et al 2019, just matching aesthetics for this manuscript.

Figure 1


Figure 1. Modeled 95% confidence interval of (A) total host energy reserves (mg cm-2), (B) cell density (106 cells cm-2), and (C) Chlorophyll a (ug cm-2) for S. siderea, P. strigosa, and P. astreoides at T0 (green) or T90 (red/blue), with individual coral fragment physiology denoted by points. Blue denotes 28°C and red denotes 31°C, with pCO2 treatment along the x axis.


Correlation assessments

Here, I am exploring the relationships between each physiology parameter measured above.


Figure 2. Correlation matrix for S. siderea, P. strigosa, and P. astreoides depicting pair-wise comparisons of physiological parameters within each species. Colour and ellipse width denote R2 of each significant comparison, and blank grids represent non-significant pair-wise comparisons (P > 0.05).Each parameter is denoted in blue text along the diagonal of each plot. Correlations with R2 above 0.5 (shown in orange and red in matrix plot) are explored further below.


## quartz_off_screen 
##                 2

Siderastrea siderea


Pseudodiploria strigosa


Porites astreoides


Holobiont PCAs

Siderastrea siderea

## Permutation test for adonis under reduced model
## Marginal effects of terms
## Permutation: free
## Number of permutations: 2000
## 
## adonis2(formula = sid_pca_df ~ fpco2 + ftemp + reef, data = s_df, permutations = bootnum, method = "eu", by = "margin")
##          Df SumOfSqs      R2       F    Pr(>F)    
## fpco2     3   145878 0.26441 11.1189 0.0004998 ***
## ftemp     1    25935 0.04701  5.9304 0.0039980 ** 
## reef      1    26681 0.04836  6.1009 0.0049975 ** 
## Residual 80   349861 0.63413                      
## Total    85   551720 1.00000                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pseudodiploria strigosa

## Permutation test for adonis under reduced model
## Marginal effects of terms
## Permutation: free
## Number of permutations: 2000
## 
## adonis2(formula = dip_pca_df ~ reef + fpco2 + ftemp, data = p_df, permutations = bootnum, method = "eu", by = "margin")
##          Df SumOfSqs      R2       F    Pr(>F)    
## reef      1   167951 0.08250 11.3347 0.0009995 ***
## fpco2     3   213070 0.10466  4.7932 0.0009995 ***
## ftemp     1   625389 0.30720 42.2061 0.0004998 ***
## Residual 71  1052041 0.51677                      
## Total    76  2035789 1.00000                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Porites astreoides

## Permutation test for adonis under reduced model
## Marginal effects of terms
## Permutation: free
## Number of permutations: 2000
## 
## adonis2(formula = por_pca_df ~ reef + ftemp + fpco2, data = a_df, permutations = bootnum, method = "eu", by = "margin")
##          Df SumOfSqs      R2       F    Pr(>F)    
## reef      1      969 0.00287  0.3246 0.7081459    
## ftemp     1    52002 0.15388 17.4102 0.0004998 ***
## fpco2     3    96015 0.28412 10.7153 0.0004998 ***
## Residual 62   185186 0.54799                      
## Total    67   337935 1.00000                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Figure 2

SSID subset PCA

## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 2000
## 
## adonis2(formula = s_df_sub[, c(14:17, 21:23, 29)] ~ fpco2 + fpco2:domSymb + domSymb + ftemp, data = s_df_sub, permutations = bootnum, method = "eu")
##               Df SumOfSqs      R2       F    Pr(>F)    
## fpco2          3    60040 0.26137 12.2655 0.0004998 ***
## domSymb        2    76699 0.33389 23.5033 0.0004998 ***
## ftemp          1    13933 0.06065  8.5391 0.0009995 ***
## fpco2:domSymb  6    28458 0.12389  2.9068 0.0009995 ***
## Residual      31    50582 0.22020                      
## Total         43   229711 1.00000                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Plasticity analyses

S. siderea

## `summarise()` ungrouping output (override with `.groups` argument)
## # Comparison of Model Performance Indices
## 
## Name           | Model |     AIC |     BIC |    R2 | R2 (adj.) |  RMSE | Sigma | Performance-Score
## --------------------------------------------------------------------------------------------------
## ssid_dist_mod  |    lm | 166.373 | 182.595 | 0.229 |     0.173 | 0.668 | 0.697 |            99.97%
## ssid_dist_mod3 |    lm | 170.343 | 184.248 | 0.165 |     0.117 | 0.695 | 0.720 |            73.96%
## ssid_dist_mod2 |    lm | 271.007 | 287.229 | 0.229 |     0.173 | 1.342 | 1.400 |            37.84%
## ssid_dist_mod4 |    lm | 276.960 | 290.865 | 0.143 |     0.094 | 1.415 | 1.465 |             0.00%

## 
## Call:
## lm(formula = log(dist) ~ fpco2 + ftemp + reef, data = sid_dist)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.08433 -0.28862  0.01186  0.32752  1.47527 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.565379   0.197374   2.865  0.00553 **
## fpco2420    -0.005195   0.288633  -0.018  0.98569   
## fpco2680    -0.034515   0.223226  -0.155  0.87757   
## fpco23300    0.659994   0.221422   2.981  0.00397 **
## ftemp31      0.055395   0.175208   0.316  0.75283   
## reefN       -0.386667   0.161708  -2.391  0.01953 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6967 on 69 degrees of freedom
## Multiple R-squared:  0.2288, Adjusted R-squared:  0.1729 
## F-statistic: 4.095 on 5 and 69 DF,  p-value: 0.002585
## Loading required namespace: qqplotr
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `summarise()` regrouping output by 'treat_plot' (override with `.groups` argument)



P. strigosa

## `summarise()` ungrouping output (override with `.groups` argument)
## # Comparison of Model Performance Indices
## 
## Name           | Model |     AIC |     BIC |    R2 | R2 (adj.) |  RMSE | Sigma | Performance-Score
## --------------------------------------------------------------------------------------------------
## pstr_dist_mod2 |    lm | 151.079 | 185.229 | 0.233 |     0.061 | 0.561 | 0.625 |            70.34%
## pstr_dist_mod4 |    lm | 148.080 | 166.293 | 0.106 |     0.024 | 0.605 | 0.637 |            66.85%
## pstr_dist_mod3 |    lm | 149.721 | 170.211 | 0.111 |     0.014 | 0.604 | 0.641 |            53.71%
## pstr_dist_mod  |    lm | 151.928 | 167.865 | 0.031 |    -0.043 | 0.631 | 0.659 |            15.28%

## 
## Call:
## lm(formula = log(dist) ~ fpco2 * ftemp * reef, data = dip_dist)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.47623 -0.38430 -0.05872  0.37259  1.46982 
## 
## Coefficients: (2 not defined because of singularities)
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              0.475309   0.197643   2.405  0.01939 * 
## fpco2420                 0.194384   0.456437   0.426  0.67178   
## fpco2680                 0.119971   0.296465   0.405  0.68721   
## fpco23300               -0.170264   0.287169  -0.593  0.55555   
## ftemp31                  0.310618   0.342328   0.907  0.36797   
## reefN                    0.878481   0.322750   2.722  0.00856 **
## fpco2420:ftemp31               NA         NA      NA       NA   
## fpco2680:ftemp31        -0.469504   0.544266  -0.863  0.39189   
## fpco23300:ftemp31        0.473575   0.488587   0.969  0.33643   
## fpco2420:reefN           0.006716   0.708029   0.009  0.99246   
## fpco2680:reefN          -0.836043   0.467012  -1.790  0.07864 . 
## fpco23300:reefN         -0.831141   0.461167  -1.802  0.07670 . 
## ftemp31:reefN           -1.303040   0.529103  -2.463  0.01678 * 
## fpco2420:ftemp31:reefN         NA         NA      NA       NA   
## fpco2680:ftemp31:reefN   1.248932   0.848178   1.472  0.14630   
## fpco23300:ftemp31:reefN  0.911877   0.772523   1.180  0.24266   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.625 on 58 degrees of freedom
## Multiple R-squared:  0.233,  Adjusted R-squared:  0.0611 
## F-statistic: 1.355 on 13 and 58 DF,  p-value: 0.2093
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `summarise()` regrouping output by 'treat_plot' (override with `.groups` argument)



P. astreoides

## `summarise()` ungrouping output (override with `.groups` argument)
## # Comparison of Model Performance Indices
## 
## Name           | Model |     AIC |     BIC |    R2 | R2 (adj.) |  RMSE | Sigma | Performance-Score
## --------------------------------------------------------------------------------------------------
## past_dist_mod5 |    lm | 106.292 | 118.550 | 0.065 |    -0.007 | 0.553 | 0.579 |            79.27%
## past_dist_mod3 |    lm | 111.283 | 129.671 | 0.082 |    -0.050 | 0.548 | 0.592 |            76.77%
## past_dist_mod  |    lm | 119.162 | 147.765 | 0.115 |    -0.126 | 0.538 | 0.613 |            74.33%
## past_dist_mod2 |    lm | 119.162 | 147.765 | 0.115 |    -0.126 | 0.538 | 0.613 |            74.33%
## past_dist_mod4 |    lm | 192.303 | 204.562 | 0.096 |     0.027 | 1.177 | 1.232 |            27.11%

## 
## Call:
## lm(formula = log(dist) ~ fpco2 + ftemp, data = por_dist)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6110 -0.3076  0.1067  0.3932  1.1107 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.56933    0.15308   3.719 0.000491 ***
## fpco2420    -0.20237    0.29678  -0.682 0.498332    
## fpco2680     0.04701    0.19656   0.239 0.811918    
## fpco23300    0.17055    0.20256   0.842 0.403651    
## ftemp31      0.30038    0.17213   1.745 0.086883 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5793 on 52 degrees of freedom
## Multiple R-squared:  0.06514,    Adjusted R-squared:  -0.006773 
## F-statistic: 0.9058 on 4 and 52 DF,  p-value: 0.4675
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `summarise()` regrouping output by 'treat_plot' (override with `.groups` argument)



Supplemental Tables

Table 1. T90 modeled mean coral host protein content and 95% confidence intervals for each species
treatment N mean lower 95% upper 95%
(a) SSID
300_28 11 0.54 0.47 0.60
300_31 9 0.48 0.41 0.55
3300_28 12 0.43 0.37 0.50
3300_31 12 0.38 0.31 0.44
420_28 12 0.50 0.43 0.57
420_31 12 0.45 0.38 0.51
680_28 13 0.46 0.39 0.53
680_31 12 0.40 0.34 0.47
(b) PSTR
300_28 16 0.53 0.47 0.59
300_31 9 0.27 0.19 0.35
3300_28 16 0.43 0.36 0.49
3300_31 8 0.17 0.09 0.24
420_28 5 0.49 0.42 0.56
420_31 6 0.23 0.15 0.32
680_28 14 0.45 0.39 0.51
680_31 5 0.19 0.11 0.28
(c) PAST
300_28 11 0.23 0.17 0.30
300_31 6 0.19 0.10 0.27
3300_28 12 0.13 0.06 0.20
3300_31 4 0.08 0.00 0.17
420_28 12 0.20 0.13 0.26
420_31 7 0.15 0.07 0.23
680_28 10 0.16 0.09 0.22
680_31 9 0.11 0.03 0.19
Table 1. T90 modeled mean coral host lipid content and 95% confidence intervals for each species
treatment N mean lower 95% upper 95%
(a) SSID
300_28 11 0.37 0.30 0.44
300_31 9 0.35 0.28 0.42
3300_28 12 0.37 0.30 0.44
3300_31 12 0.35 0.28 0.42
420_28 12 0.37 0.30 0.44
420_31 12 0.35 0.27 0.42
680_28 13 0.38 0.31 0.45
680_31 12 0.36 0.29 0.43
(b) PSTR
300_28 16 0.24 0.17 0.31
300_31 9 0.11 0.02 0.20
3300_28 15 0.24 0.17 0.31
3300_31 8 0.10 0.02 0.19
420_28 5 0.24 0.17 0.31
420_31 5 0.11 0.02 0.20
680_28 14 0.24 0.17 0.31
680_31 5 0.11 0.02 0.20
(c) PAST
300_28 11 0.15 0.08 0.23
300_31 6 0.20 0.11 0.29
3300_28 12 0.16 0.08 0.23
3300_31 4 0.22 0.14 0.31
420_28 12 0.16 0.08 0.23
420_31 7 0.20 0.11 0.29
680_28 10 0.16 0.08 0.23
680_31 9 0.20 0.11 0.29
Table 1. T90 modeled mean coral host carbohydrate content and 95% confidence intervals for each species
treatment N mean lower 95% upper 95%
(a) SSID
300_28 11 1.15 0.95 1.35
300_31 8 0.82 0.61 1.02
3300_28 12 1.10 0.91 1.29
3300_31 12 0.77 0.60 0.96
420_28 12 1.08 0.90 1.26
420_31 12 0.75 0.57 0.94
680_28 13 1.27 1.10 1.45
680_31 12 0.94 0.75 1.11
(b) PSTR
300_28 16 0.77 0.60 0.93
300_31 9 0.50 0.30 0.68
3300_28 16 0.62 0.45 0.78
3300_31 8 0.34 0.15 0.55
420_28 5 0.67 0.41 0.92
420_31 6 0.40 0.16 0.65
680_28 14 0.56 0.37 0.75
680_31 7 0.29 0.07 0.51
(c) PAST
300_28 11 0.82 0.61 1.03
300_31 6 0.65 0.41 0.88
3300_28 12 0.58 0.38 0.78
3300_31 4 0.41 0.16 0.65
420_28 12 0.90 0.71 1.10
420_31 7 0.73 0.51 0.95
680_28 10 0.61 0.41 0.81
680_31 9 0.43 0.23 0.64
Table 1. T90 modeled mean coral symbiont density content and 95% confidence intervals for each species
treatment N mean lower 95% upper 95%
(a) SSID
300_28 11 3.32 2.23 4.46
300_31 9 2.45 1.33 3.58
3300_28 12 2.04 0.97 3.07
3300_31 12 1.18 0.12 2.23
420_28 12 3.48 2.42 4.50
420_31 12 2.61 1.55 3.67
680_28 13 2.96 1.95 3.98
680_31 12 2.10 1.04 3.14
(b) PSTR
300_28 16 2.16 1.14 3.15
300_31 9 0.42 -0.77 1.60
3300_28 16 1.53 0.53 2.52
3300_31 8 -0.27 -1.48 0.89
420_28 5 2.16 0.75 3.61
420_31 6 0.45 -0.96 1.86
680_28 14 1.71 0.68 2.75
680_31 7 -0.09 -1.30 1.14
(c) PAST
300_28 11 7.29 6.13 8.48
300_31 6 6.42 5.02 7.74
3300_28 12 5.92 4.74 7.16
3300_31 4 4.86 3.51 6.15
420_28 12 6.43 5.28 7.57
420_31 6 5.51 4.22 6.83
680_28 10 5.09 3.84 6.35
680_31 8 4.19 2.87 5.45
Table 1. T90 modeled mean coral symbiont chlorophyll a content and 95% confidence intervals for each species
treatment N mean lower 95% upper 95%
(a) SSID
300_28 11 112.38 81.74 143.62
300_31 9 105.47 71.18 140.02
3300_28 12 48.52 17.15 79.21
3300_31 12 32.61 3.07 63.16
420_28 12 155.21 122.77 186.81
420_31 12 77.84 46.62 108.58
680_28 13 83.24 53.49 114.40
680_31 12 82.41 51.78 113.66
(b) PSTR
300_28 16 185.93 157.24 214.55
300_31 9 120.37 85.65 154.64
3300_28 16 78.53 51.36 106.93
3300_31 8 -1.42 -37.11 34.23
420_28 5 161.17 118.71 202.49
420_31 6 26.74 -14.58 66.79
680_28 14 84.10 54.62 114.41
680_31 5 17.96 -22.30 58.03
(c) PAST
300_28 11 97.02 63.84 130.54
300_31 6 155.01 116.85 192.29
3300_28 12 15.56 -18.42 45.96
3300_31 4 61.04 19.29 101.45
420_28 12 64.66 33.23 97.25
420_31 7 51.82 15.19 89.83
680_28 10 33.69 1.31 67.61
680_31 9 96.83 62.24 133.28
Table 1. T90 modeled mean coral host energy reserves and 95% confidence intervals for each species
treatment N mean lower 95% upper 95%
(a) SSID
300_28 11 2.02 1.61 2.43
300_31 8 1.62 1.21 2.04
3300_28 12 1.92 1.56 2.28
3300_31 12 1.53 1.17 1.89
420_28 12 2.02 1.65 2.40
420_31 12 1.58 1.21 1.95
680_28 13 2.06 1.70 2.41
680_31 12 1.65 1.29 2.01
(b) PSTR
300_28 16 1.60 1.25 1.94
300_31 9 0.96 0.58 1.35
3300_28 15 1.28 0.92 1.64
3300_31 8 0.60 0.20 0.99
420_28 5 1.39 0.84 1.94
420_31 5 0.71 0.14 1.27
680_28 14 1.23 0.82 1.62
680_31 5 0.56 0.11 1.00
(c) PAST
300_28 11 1.26 0.84 1.69
300_31 6 1.12 0.65 1.60
3300_28 12 0.86 0.41 1.29
3300_31 4 0.56 0.13 0.98
420_28 12 1.26 0.86 1.66
420_31 7 1.14 0.71 1.58
680_28 10 0.85 0.44 1.25
680_31 9 0.72 0.31 1.14


Session information

Session information from the last run date on 2021-04-05:

## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.7
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] png_0.1-7          MASS_7.3-53        performance_0.7.0  wesanderson_0.3.6 
##  [5] RColorBrewer_1.1-2 gridGraphics_0.5-1 corrplot_0.84      Hmisc_4.4-2       
##  [9] Formula_1.2-4      survival_3.2-7     magick_2.5.2       ggpubr_0.4.0      
## [13] vroom_1.3.2        lmerTest_3.1-3     lme4_1.1-26        Matrix_1.3-2      
## [17] kableExtra_1.3.1   ggfortify_0.4.11   cowplot_1.1.1      Rmisc_1.5         
## [21] shiny_1.5.0        vegan_2.5-7        lattice_0.20-41    permute_0.9-5     
## [25] forcats_0.5.0      stringr_1.4.0      purrr_0.3.4        tibble_3.0.4      
## [29] tidyverse_1.3.0    plotly_4.9.3       openxlsx_4.2.3     tidyr_1.1.2       
## [33] ggbiplot_0.55      scales_1.1.1       plyr_1.8.6         dplyr_1.0.2       
## [37] ggplot2_3.3.3      readr_1.4.0        knitr_1.30        
## 
## loaded via a namespace (and not attached):
##  [1] readxl_1.3.1        backports_1.2.1     lazyeval_0.2.2     
##  [4] splines_3.6.3       qqplotr_0.0.4       digest_0.6.27      
##  [7] htmltools_0.5.1     fansi_0.4.1         magrittr_2.0.1     
## [10] checkmate_2.0.0     cluster_2.1.0       see_0.6.2          
## [13] modelr_0.1.8        jpeg_0.1-8.1        colorspace_2.0-0   
## [16] rvest_0.3.6         ggrepel_0.9.0       haven_2.3.1        
## [19] xfun_0.20           crayon_1.3.4        jsonlite_1.7.2     
## [22] glue_1.4.2          gtable_0.3.0        webshot_0.5.2      
## [25] car_3.0-10          DEoptimR_1.0-8      abind_1.4-5        
## [28] DBI_1.1.0           rstatix_0.6.0       Rcpp_1.0.5         
## [31] viridisLite_0.3.0   xtable_1.8-4        htmlTable_2.1.0    
## [34] foreign_0.8-75      bit_4.0.4           htmlwidgets_1.5.3  
## [37] httr_1.4.2          ellipsis_0.3.1      pkgconfig_2.0.3    
## [40] farver_2.0.3        nnet_7.3-14         dbplyr_2.0.0       
## [43] tidyselect_1.1.0    labeling_0.4.2      rlang_0.4.10       
## [46] later_1.1.0.1       effectsize_0.4.1    munsell_0.5.0      
## [49] cellranger_1.1.0    tools_3.6.3         cli_2.2.0          
## [52] generics_0.1.0      broom_0.7.3         ggridges_0.5.3     
## [55] evaluate_0.14       fastmap_1.0.1       yaml_2.2.1         
## [58] bit64_4.0.5         fs_1.5.0            robustbase_0.93-7  
## [61] zip_2.1.1           nlme_3.1-151        mime_0.9           
## [64] xml2_1.3.2          compiler_3.6.3      rstudioapi_0.13    
## [67] curl_4.3            ggsignif_0.6.0      reprex_0.3.0       
## [70] statmod_1.4.35      stringi_1.5.3       highr_0.8          
## [73] parameters_0.10.1   nloptr_1.2.2.2      vctrs_0.3.6        
## [76] pillar_1.4.7        lifecycle_0.2.0     data.table_1.13.6  
## [79] insight_0.13.1      httpuv_1.5.5        R6_2.5.0           
## [82] latticeExtra_0.6-29 promises_1.1.1      gridExtra_2.3      
## [85] rio_0.5.16          boot_1.3-25         assertthat_0.2.1   
## [88] withr_2.3.0         mgcv_1.8-33         bayestestR_0.8.0   
## [91] parallel_3.6.3      hms_1.0.0           rpart_4.1-15       
## [94] minqa_1.2.4         rmarkdown_2.6       carData_3.0-4      
## [97] numDeriv_2016.8-1.1 lubridate_1.7.9.2   base64enc_0.1-3